An Accurate Reservoir's Bubble Point Pressure Correlation
Author | Alakbari, Fahd Saeed |
Author | Mohyaldinn, Mysara Eissa |
Author | Ayoub, Mohammed Abdalla |
Author | Muhsan, Ali Samer |
Author | Hussein, Ibnelwaleed A. |
Available date | 2023-07-12T07:28:16Z |
Publication Date | 2022 |
Publication Name | ACS Omega |
Resource | Scopus |
Abstract | Bubble point pressure (Pb) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The Pbcan be measured from the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. Therefore, some studies used alternative methods, namely, empirical correlations and machine learning techniques, to obtain the Pb. However, the previously published methods have restrictions like accuracy, and some use specific data to build their models. In addition, most of the previously published models have not shown the proper relationships between the features and targets to indicate the correct physical behavior. Therefore, this study develops an accurate and robust correlation to obtain the Pbapplying the Group Method of Data Handling (GMDH). The GMDH combines neural networks and statistical methods that generate relationships among the feature and target parameters. A total of 760 global datasets were used to develop the GMDH model. The GMDH model is verified using trend analysis and indicates that the GMDH model follows all input parameters' exact physical behavior. In addition, different statistical analyses were conducted to investigate the GMDH and the published models' robustness. The GMDH model follows the correct trend for four input parameters (gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature). The GMDH correlation has the lowest average percent relative error, root mean square error, and standard deviation of 8.51%, 12.70, and 0.09, respectively, and the highest correlation coefficient of 0.9883 compared to published models. The different statistical analyses indicated that the GMDH is the first rank model to accurately and robustly predict the Pb 2022 American Chemical Society. All rights reserved. |
Sponsor | The authors would like to give their sincerest thanks to the Universiti Teknologi PETRONAS for supporting this study under YUTP-Grant cost center 015LC0-105. |
Language | en |
Publisher | American Chemical Society |
Subject | Bubbles Layers Lipids Neural networks Solubility |
Type | Article |
Pagination | 13196-13209 |
Issue Number | 15 |
Volume Number | 7 |
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Chemical Engineering [1141 items ]
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GPC Research [494 items ]